Course Content for R-Language Course Objective R Language is programmer for statistical analysis. Statistical Analysis plays major role in the Data Analysis. R is support to research and analysis purpose. It is free source software using this we can do machine learning concepts, statistical analysis and deployment to the researcher. S.No Topic Detailed Syllabus 1 Introduction to R About R, working with tool and explanation 2 Basic Data Types Data types in R(Numeric, Integer, Complex, Logical, Character) and how to use this Data types in R 3 Objects Creating Objects(Vectors, Matrices, Factors, Arrays, Data frames),uses and creating objects 4 Functions and Loops Etc., Creating, accessing function in R 5 Data Preparation Data Coding, Data Cleaning, Identification Outlier, Handling Missing Values 6 Statistics Basics concepts in statistics 7 Regression & Types of Regression Linear Regression, Multiple Regression , Logistic Regression 8 Classification Methods Discriminant Analysis – Basics, Rotation Methods involved in DA. 9 Cluster Analysis Cluster Analysis Basics and real life Examples 10 Analysis of Variance Anova , Manova 11 Survival Analysis Types of Non parametric test in Survival and Cox proportional Hazard Rate Model 12 Time series Forecasting 13 Machine Learning Algorithms and Coding for Concepts in ML 14 Backup Classes Doubts clarification for all Session

1. Course Content for R-Language
Course Objective
R Language is programmer for statistical analysis. Statistical Analysis plays major role in the Data Analysis.
R is support to research and analysis purpose. It is free source software using this we can do machine
learning concepts, statistical analysis and deployment to the researcher.
S.No Topic Detailed Syllabus
1 Introduction to R About R, working with tool and explanation
2 Basic Data Types
Data types in R(Numeric, Integer, Complex, Logical, Character)
and how to use this Data types in R
3 Objects
Creating Objects(Vectors, Matrices, Factors, Arrays, Data
frames),uses and creating objects
4 Functions and Loops Etc., Creating, accessing function in R
5 Data Preparation
Data Coding, Data Cleaning, Identification Outlier, Handling
Missing Values
6 Statistics Basics concepts in statistics
7 Regression & Types of Regression Linear Regression, Multiple Regression , Logistic Regression
8 Classification Methods
Discriminant Analysis – Basics, Rotation Methods involved in
DA.
9 Cluster Analysis Cluster Analysis Basics and real life Examples
10 Analysis of Variance Anova , Manova
11 Survival Analysis
Types of Non parametric test in Survival and Cox proportional
Hazard Rate Model
12 Time series Forecasting
13 Machine Learning Algorithms and Coding for Concepts in ML
14 Backup Classes Doubts clarification for all Session
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